SEO

How to Reverse Video Search (& Why It’s Useful)

Published

2 years ago

October 4, 2022

Entireweb News Bot

How to Reverse Video Search (& Why It's Useful)

Have you ever stumbled across an exciting video and wondered where it came from?

If so, you’ll be pleased to know that there are many ways to find a video source through reverse video searches.

This guide teaches how to conduct a reverse video search and why it’s useful.

What Is A Reverse Video Search?

Many people use search engines to find information by searching for a particular word or phrase (i.e., “keywords”) until the search engine reveals a page, video, or another piece of content that matches the search.

A reverse search, however, involves entering the piece of content (typically, a video or image) and finding each webpage on which the piece of content appears.

Thus, an RVS (reverse video search) involves imputing a video into the search engine and discovering the source of that video on the web.

How Reverse Video Search Works

Search engines, like Google, can interpret the color and pixels in a video and then find similar (or exact) videos on the web.

This will often reveal the original source of the video and any other instances of the video online.

However, this process is not always 100% accurate.

If a single pixel in the video has changed, it might not appear in the search results.

Plus, a high number of videos are uploaded to the web every day, so this process requires search engines to effectively index all videos to surface them in the search results.

Reasons To Use Reverse Video Search

There are a few reasons one might want to use reverse video search. Here are the most common use cases:

Find The Source Of A Video

Most often, a reverse video search is used to find the source of a video.

Say you find a funny or valuable video online. You might want to know who published the video, whether other content (like a blog article) is connected to the video, or whether the owner produces similar content.

A reverse video search, in this instance, may be able to find the original source of the content.

Find Duplicate Videos

If you are a video producer, you might want to use a reverse video search to see if anyone has copied or reproduced one of your original videos.

A reverse video search can help you find illegitimate uses of your content, after which you can contact the owner and ask for credit or for the video to be taken down.

Find The Full Version Of A Video Clip

Sometimes you might come across an interesting video clip and want to find the rest of the video.

A reverse video search might be able to interpret the video clip and find the full video online.

Find Related Content

A reverse video search may also be able to help you discover content that’s related to the video.

It might surface similar videos or related content (like articles, web pages, or blog posts) that featured the video.

This can be a great way to find more interesting content.

How To Conduct A Reverse Video Search

There are many ways to conduct a reverse video search. It often requires using the search engine directly or a third-party tool to upload the image.

Here are the most effective methods for conducting a reverse video search.

Run A Reverse Video Search On Google

Google doesn’t offer a reverse search function specific to video, so you will need to take a screenshot of the video and then use the reverse image search function.

Find a distinctive frame in the video (i.e., a section that seems unique from other videos and most likely to surface the same video online).
Pause the video.
Take a screenshot of the frame you wish to capture ( Shift-Command-4 on Apple/Mac or Ctrl + PrtScn on Windows).
Save the screenshot.
Navigate to Google Images and select the camera icon. Use the search by image option.
Upload the screenshot.
Google will return the search results for your screenshot (if available).

Screenshot of Google Images search engine, September 2022

Google Images search results for cat videos

Screenshot of Google Images results for [domestic short-haired cat], Google, September 2022

Run A Reverse Video Search Using Berify

Berify.com is a reverse image and video search tool that matches your search to results from several search engines at once, including Google, Bing, Yandex, and others.

This may provide more complete results than using a single search engine.

Note: This freemium tool allows you to sign up for free, but will then charge a monthly subscription fee. So, use the free version if you only need a few searches.

Here’s how to use it:

Take a screenshot of the video clip you want to search for.
Visit Berify.com.
Upload the screenshot to the search box that says Browse and upload the image here.
Click Search.
Berify will surface any results that match your search.

Run A Reverse Video Search Using Shutterstock

Shutterstock hosts a massive online database of over 1 billion images and videos. It can also be used to conduct a reverse video search.

Take a screenshot of the video clip you want to search for.
Visit Shutterstock.com.
Navigate to the search box. Click on the camera icon (the Search by image function).
Upload the screenshot. (Note: You can also specify whether you’re searching for certain vectors or whether illustrations in the video are animated/computer generated).
Click the magnifying glass.
Shutterstock will surface images or videos similar to your search.

Run A Reverse Video Search Using TinEye

TinEye is one of the leading “search by image” tools that allow you to find other images and videos that match your search.

TinEye uses computer vision, image recognition, and reverse image search technology.

Take a screenshot of the video clip you want to search for or search for the video by URL.
Visit TinEye.com.
Find the search box. Click the Upload button to upload your screenshot, or simply drag and drop your image.
Click the magnifying glass.
TinEye will surface any images or videos that are similar to your search.

Run A Reverse Video Search on Bing

Like Google, Bing’s reverse video search function works best with a video screenshot. Running a reverse video search on Bing is simple:

Take a screenshot of the video clip you want to search for.
Open Bing’s Visual Search page.
Upload the screenshot, drag and drop the screenshot, or paste the URL of the image or video in the search box.
Bing will surface results for “related content” that closely matches the image or video.

Conducting A Reverse Image Search is Simple

Whether you’re trying to track down the source of a funny video or find similar content to suit your interests, a reverse video search can be a helpful tool for anyone.

Google, Bing, TinEye, and other tools offer reverse video search features that simplify finding a video’s origins.

Remember, reverse video search can help you find duplicate content, which could help you protect your digital assets. Or, it can help you find the original publisher of a video so you can give credit where it’s due.

Video is an excellent addition to many marketing campaigns, web content, social media strategy, and more. Use reverse video search to make finding, sourcing, and attributing videos more accessible than ever.

More resources:

Featured Image: Overearth/Shutterstock

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SEO

How Compression Can Be Used To Detect Low Quality Pages

Published

6 days ago

October 27, 2024

Max

Compression can be used by search engines to detect low-quality pages. Although not widely known, it's useful foundational knowledge for SEO.

The concept of Compressibility as a quality signal is not widely known, but SEOs should be aware of it. Search engines can use web page compressibility to identify duplicate pages, doorway pages with similar content, and pages with repetitive keywords, making it useful knowledge for SEO.

Although the following research paper demonstrates a successful use of on-page features for detecting spam, the deliberate lack of transparency by search engines makes it difficult to say with certainty if search engines are applying this or similar techniques.

What Is Compressibility?

In computing, compressibility refers to how much a file (data) can be reduced in size while retaining essential information, typically to maximize storage space or to allow more data to be transmitted over the Internet.

TL/DR Of Compression

Compression replaces repeated words and phrases with shorter references, reducing the file size by significant margins. Search engines typically compress indexed web pages to maximize storage space, reduce bandwidth, and improve retrieval speed, among other reasons.

This is a simplified explanation of how compression works:

Identify Patterns:
A compression algorithm scans the text to find repeated words, patterns and phrases
Shorter Codes Take Up Less Space:
The codes and symbols use less storage space then the original words and phrases, which results in a smaller file size.
Shorter References Use Less Bits:
The “code” that essentially symbolizes the replaced words and phrases uses less data than the originals.

A bonus effect of using compression is that it can also be used to identify duplicate pages, doorway pages with similar content, and pages with repetitive keywords.

Research Paper About Detecting Spam

This research paper is significant because it was authored by distinguished computer scientists known for breakthroughs in AI, distributed computing, information retrieval, and other fields.

Marc Najork

One of the co-authors of the research paper is Marc Najork, a prominent research scientist who currently holds the title of Distinguished Research Scientist at Google DeepMind. He’s a co-author of the papers for TW-BERT, has contributed research for increasing the accuracy of using implicit user feedback like clicks, and worked on creating improved AI-based information retrieval (DSI++: Updating Transformer Memory with New Documents), among many other major breakthroughs in information retrieval.

Dennis Fetterly

Another of the co-authors is Dennis Fetterly, currently a software engineer at Google. He is listed as a co-inventor in a patent for a ranking algorithm that uses links, and is known for his research in distributed computing and information retrieval.

Those are just two of the distinguished researchers listed as co-authors of the 2006 Microsoft research paper about identifying spam through on-page content features. Among the several on-page content features the research paper analyzes is compressibility, which they discovered can be used as a classifier for indicating that a web page is spammy.

Detecting Spam Web Pages Through Content Analysis

Although the research paper was authored in 2006, its findings remain relevant to today.

Then, as now, people attempted to rank hundreds or thousands of location-based web pages that were essentially duplicate content aside from city, region, or state names. Then, as now, SEOs often created web pages for search engines by excessively repeating keywords within titles, meta descriptions, headings, internal anchor text, and within the content to improve rankings.

Section 4.6 of the research paper explains:

“Some search engines give higher weight to pages containing the query keywords several times. For example, for a given query term, a page that contains it ten times may be higher ranked than a page that contains it only once. To take advantage of such engines, some spam pages replicate their content several times in an attempt to rank higher.”

The research paper explains that search engines compress web pages and use the compressed version to reference the original web page. They note that excessive amounts of redundant words results in a higher level of compressibility. So they set about testing if there’s a correlation between a high level of compressibility and spam.

They write:

“Our approach in this section to locating redundant content within a page is to compress the page; to save space and disk time, search engines often compress web pages after indexing them, but before adding them to a page cache.
…We measure the redundancy of web pages by the compression ratio, the size of the uncompressed page divided by the size of the compressed page. We used GZIP …to compress pages, a fast and effective compression algorithm.”

High Compressibility Correlates To Spam

The results of the research showed that web pages with at least a compression ratio of 4.0 tended to be low quality web pages, spam. However, the highest rates of compressibility became less consistent because there were fewer data points, making it harder to interpret.

Figure 9: Prevalence of spam relative to compressibility of page.

The researchers concluded:

“70% of all sampled pages with a compression ratio of at least 4.0 were judged to be spam.”

But they also discovered that using the compression ratio by itself still resulted in false positives, where non-spam pages were incorrectly identified as spam:

“The compression ratio heuristic described in Section 4.6 fared best, correctly identifying 660 (27.9%) of the spam pages in our collection, while misidentifying 2, 068 (12.0%) of all judged pages.
Using all of the aforementioned features, the classification accuracy after the ten-fold cross validation process is encouraging:
95.4% of our judged pages were classified correctly, while 4.6% were classified incorrectly.
More specifically, for the spam class 1, 940 out of the 2, 364 pages, were classified correctly. For the non-spam class, 14, 440 out of the 14,804 pages were classified correctly. Consequently, 788 pages were classified incorrectly.”

The next section describes an interesting discovery about how to increase the accuracy of using on-page signals for identifying spam.

Insight Into Quality Rankings

The research paper examined multiple on-page signals, including compressibility. They discovered that each individual signal (classifier) was able to find some spam but that relying on any one signal on its own resulted in flagging non-spam pages for spam, which are commonly referred to as false positive.

The researchers made an important discovery that everyone interested in SEO should know, which is that using multiple classifiers increased the accuracy of detecting spam and decreased the likelihood of false positives. Just as important, the compressibility signal only identifies one kind of spam but not the full range of spam.

The takeaway is that compressibility is a good way to identify one kind of spam but there are other kinds of spam that aren’t caught with this one signal. Other kinds of spam were not caught with the compressibility signal.

This is the part that every SEO and publisher should be aware of:

“In the previous section, we presented a number of heuristics for assaying spam web pages. That is, we measured several characteristics of web pages, and found ranges of those characteristics which correlated with a page being spam. Nevertheless, when used individually, no technique uncovers most of the spam in our data set without flagging many non-spam pages as spam.
For example, considering the compression ratio heuristic described in Section 4.6, one of our most promising methods, the average probability of spam for ratios of 4.2 and higher is 72%. But only about 1.5% of all pages fall in this range. This number is far below the 13.8% of spam pages that we identified in our data set.”

So, even though compressibility was one of the better signals for identifying spam, it still was unable to uncover the full range of spam within the dataset the researchers used to test the signals.

Combining Multiple Signals

The above results indicated that individual signals of low quality are less accurate. So they tested using multiple signals. What they discovered was that combining multiple on-page signals for detecting spam resulted in a better accuracy rate with less pages misclassified as spam.

The researchers explained that they tested the use of multiple signals:

“One way of combining our heuristic methods is to view the spam detection problem as a classification problem. In this case, we want to create a classification model (or classifier) which, given a web page, will use the page’s features jointly in order to (correctly, we hope) classify it in one of two classes: spam and non-spam.”

These are their conclusions about using multiple signals:

“We have studied various aspects of content-based spam on the web using a real-world data set from the MSNSearch crawler. We have presented a number of heuristic methods for detecting content based spam. Some of our spam detection methods are more effective than others, however when used in isolation our methods may not identify all of the spam pages. For this reason, we combined our spam-detection methods to create a highly accurate C4.5 classifier. Our classifier can correctly identify 86.2% of all spam pages, while flagging very few legitimate pages as spam.”

Key Insight:

Misidentifying “very few legitimate pages as spam” was a significant breakthrough. The important insight that everyone involved with SEO should take away from this is that one signal by itself can result in false positives. Using multiple signals increases the accuracy.

What this means is that SEO tests of isolated ranking or quality signals will not yield reliable results that can be trusted for making strategy or business decisions.

Takeaways

We don’t know for certain if compressibility is used at the search engines but it’s an easy to use signal that combined with others could be used to catch simple kinds of spam like thousands of city name doorway pages with similar content. Yet even if the search engines don’t use this signal, it does show how easy it is to catch that kind of search engine manipulation and that it’s something search engines are well able to handle today.

Here are the key points of this article to keep in mind:

Doorway pages with duplicate content is easy to catch because they compress at a higher ratio than normal web pages.
Groups of web pages with a compression ratio above 4.0 were predominantly spam.
Negative quality signals used by themselves to catch spam can lead to false positives.
In this particular test, they discovered that on-page negative quality signals only catch specific types of spam.
When used alone, the compressibility signal only catches redundancy-type spam, fails to detect other forms of spam, and leads to false positives.
Combing quality signals improves spam detection accuracy and reduces false positives.
Search engines today have a higher accuracy of spam detection with the use of AI like Spam Brain.

Read the research paper, which is linked from the Google Scholar page of Marc Najork:

Detecting spam web pages through content analysis

Featured Image by Shutterstock/pathdoc

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SEO

New Google Trends SEO Documentation

Published

7 days ago

October 27, 2024

Max

Google publishes new documentation for how to use Google Trends for search marketing

Google Search Central published new documentation on Google Trends, explaining how to use it for search marketing. This guide serves as an easy to understand introduction for newcomers and a helpful refresher for experienced search marketers and publishers.

The new guide has six sections:

About Google Trends
Tutorial on monitoring trends
How to do keyword research with the tool
How to prioritize content with Trends data
How to use Google Trends for competitor research
How to use Google Trends for analyzing brand awareness and sentiment

The section about monitoring trends advises there are two kinds of rising trends, general and specific trends, which can be useful for developing content to publish on a site.

Using the Explore tool, you can leave the search box empty and view the current rising trends worldwide or use a drop down menu to focus on trends in a specific country. Users can further filter rising trends by time periods, categories and the type of search. The results show rising trends by topic and by keywords.

To search for specific trends users just need to enter the specific queries and then filter them by country, time, categories and type of search.

The section called Content Calendar describes how to use Google Trends to understand which content topics to prioritize.

Google explains:

“Google Trends can be helpful not only to get ideas on what to write, but also to prioritize when to publish it. To help you better prioritize which topics to focus on, try to find seasonal trends in the data. With that information, you can plan ahead to have high quality content available on your site a little before people are searching for it, so that when they do, your content is ready for them.”

Read the new Google Trends documentation:

Get started with Google Trends

Featured Image by Shutterstock/Luis Molinero

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SEO

All the best things about Ahrefs Evolve 2024

Published

1 week ago

October 25, 2024

Entireweb News Bot

All the best things about Ahrefs Evolve 2024

Hey all, I’m Rebekah and I am your Chosen One to “do a blog post for Ahrefs Evolve 2024”.

What does that entail exactly? I don’t know. In fact, Sam Oh asked me yesterday what the title of this post would be. “Is it like…Ahrefs Evolve 2024: Recap of day 1 and day 2…?”

Even as I nodded, I couldn’t get over how absolutely boring that sounded. So I’m going to do THIS instead: a curation of all the best things YOU loved about Ahrefs’ first conference, lifted directly from X.

Let’s go!

OUR HUGE SCREEN

The largest presentation screen I’ve ever seen! #ahrefsevolve pic.twitter.com/oboiMFW1TN
— Patrick Stox (@patrickstox) October 24, 2024
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This is the biggest presentation screen I ever seen in my life. It’s like iMax for SEO presentations. #ahrefsevolve pic.twitter.com/sAfZ1rtePx
— Suganthan Mohanadasan (@Suganthanmn) October 24, 2024

CONFERENCE VENUE ITSELF

It was recently named the best new skyscraper in the world, by the way.

The Ahrefs conference venue feels like being in inception. #AhrefsEvolve pic.twitter.com/18Yjai1Cej
— Suganthan Mohanadasan (@Suganthanmn) October 24, 2024

I’m in Singapore for @ahrefs Evolve this week. Keen to connect with people doing interesting work on the future of search / AI #ahrefsevolve pic.twitter.com/s00UkIbxpf
— Alex Denning (@AlexDenning) October 23, 2024

OUR AMAZING SPEAKER LINEUP – SUPER INFORMATIVE, USEFUL TALKS!

A super insightful explanation of how Google Search Ranking works #ahrefsevolve pic.twitter.com/Cd1VSET2Aj
— Amanda Walls (@amandajwalls) October 24, 2024

“would I even do this if Google didn’t exist?” – what a great question to assess if you actually have the right focus when creating content amazing presentation from @amandaecking at #AhrefsEvolve pic.twitter.com/a6OKbKxwiS
— Aleyda Solis ️ (@aleyda) October 24, 2024

Attending @CyrusShepard ‘s talk on WTF is Helpful Content in Google’s algorithm at #AhrefsEvolve
“Focus on people first content”
Super relevant for content creators who want to stay ahead of the ever evolving Google search curve! #SEOTalk #SEO pic.twitter.com/KRTL13SB0g

This is the first time I am listening to @aleyda and it is really amazing. Lot of insights and actionable information.
Thank you #aleyda for power packed presentation.#AhrefsEvolve @ahrefs #seo pic.twitter.com/Xe3A9MGfrr
— Jignesh Gohel (@jigneshgohel) October 25, 2024
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— Parth Suba (@parthsuba77) October 24, 2024

@thinking_slows thoughts on AI content – “it’s very good if you want to be average”.
We can do a lot better and Ryan explains how. Love it @ahrefs #AhrefsEvolve pic.twitter.com/qFqWs6QBH5
— Andy Chadwick (@digitalquokka) October 24, 2024

A super insightful explanation of how Google Search Ranking works #ahrefsevolve pic.twitter.com/Cd1VSET2Aj
— Amanda Walls (@amandajwalls) October 24, 2024

This is the first time I am listening to @aleyda and it is really amazing. Lot of insights and actionable information.
Thank you #aleyda for power packed presentation.#AhrefsEvolve @ahrefs #seo pic.twitter.com/Xe3A9MGfrr
— Jignesh Gohel (@jigneshgohel) October 25, 2024
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GREAT MUSIC

First time I’ve ever Shazam’d a track during SEO conference ambience…. and the track wasn’t even Shazamable! #AhrefsEvolve @ahrefs pic.twitter.com/ZDzJOZMILt
— Lily Ray (@lilyraynyc) October 24, 2024

AMAZING GOODIES

Ahrefs Evolveきました！@ahrefs @AhrefsJP #AhrefsEvolve pic.twitter.com/33EiejQPdX
— さくらぎ (@sakuragi_ksy) October 24, 2024

Aside from the very interesting topics, what makes this conference even cooler are the ton of awesome freebies
Kudos for making all of these happen for #AhrefsEvolve @ahrefs team pic.twitter.com/DGzk5FSTN8
— Krista Melgarejo (@kimelgarejo) October 24, 2024
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Content Goblin and SEO alligator party stickers are definitely going on my laptop. @ahrefs #ahrefsevolve pic.twitter.com/QBsBuY5Yix
— Patrick Stox (@patrickstox) October 24, 2024

This is one of the best swag bags I’ve received at any conference!
Either @ahrefs actually cares or the other conference swag bags aren’t up to par w Ahrefs!#AhrefsEvolve pic.twitter.com/Yc9e6wZPHn
— Moses Sanchez (@SanchezMoses) October 25, 2024

SELFIE BATTLE

Some background: Tim and Sam have a challenge going on to see who can take the most number of selfies with all of you. Last I heard, Sam was winning – but there is room for a comeback yet!

Got the rare selfie with both @timsoulo and @samsgoh #AhrefsEvolve
pic.twitter.com/057sBpYPwh
— Bernard Huang (@bernardjhuang) October 24, 2024
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THAT BELL

Everybody’s just waiting for this one.

@timsoulo @ahrefs #AhrefsEvolve pic.twitter.com/6ypWaTGDDP
— Jinbo Liang (@JinboLiang) October 24, 2024

STICKER WALL

Viva la vida, viva Seo!
Awante Argentina loco!#AhrefsEvolve pic.twitter.com/sfhbI2kWSH
— Gaston Riera. (@GastonRiera) October 24, 2024

AND, OF COURSE…ALL OF YOU!

#AhrefsEvolve let’s goooooooooooo!!! pic.twitter.com/THtdvdtUyB
— Tim Soulo (@timsoulo) October 24, 2024

–

There’s a TON more content on LinkedIn – click here – but I have limited time to get this post up and can’t quite figure out how to embed LinkedIn posts so…let’s stop here for now. I’ll keep updating as we go along!

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